breveWalker is an experiment in artificial intelligence and robotics which uses a genetic algorithm to teach a legged robot how to walk. Starting with completely random behaviors, the system uses evolution to discover walking strategies which carry the robot the farthest.

A genetic algorithm is an artificial intelligence technique, inspired by nature, in which solutions to a problem are evolved over time using selection, mutation and genetic recombination.

Starting with completely randomized data, a genetic algorithm tests potential solutions one by one, breeding and mutating those that preform well and discarding those that do not. Over time, the randomized data evolves into robust solutions to problem that the genetic algorithm is trying to solve.

In breveWalker, the genetic algorithm is attempting to solve the problem of finding efficient walking behaviors for a realistic, physically-simulated, legged robot. The walking behaviors are encoded as a series of variables that influence the movement of each joint in the robot. The genetic algorithm picks the best potential solutions by looking at the distance that the walker travels while controlled by a given individual.

NOTE! Evolving effective walkers can require a while to run, but progress is saved between launches of the app.